no code implementations • CVPR 2013 • Luc Oth, Paul Furgale, Laurent Kneip, Roland Siegwart
Rolling Shutter (RS) cameras are used across a wide range of consumer electronic devices--from smart-phones to high-end cameras.
no code implementations • CVPR 2016 • Elena Stumm, Christopher Mei, Simon Lacroix, Juan Nieto, Marco Hutter, Roland Siegwart
A novel method for visual place recognition is introduced and evaluated, demonstrating robustness to perceptual aliasing and observation noise.
2 code implementations • 25 Sep 2016 • Renaud Dubé, Daniel Dugas, Elena Stumm, Juan Nieto, Roland Siegwart, Cesar Cadena
We propose SegMatch, a reliable loop-closure detection algorithm based on the matching of 3D segments.
Robotics
no code implementations • 11 Oct 2016 • Mathias Gehrig, Elena Stumm, Timo Hinzmann, Roland Siegwart
We propose a novel scoring concept for visual place recognition based on nearest neighbor descriptor voting and demonstrate how the algorithm naturally emerges from the problem formulation.
3 code implementations • 11 Nov 2016 • Helen Oleynikova, Zachary Taylor, Marius Fehr, Juan Nieto, Roland Siegwart
We show that we can build TSDFs faster than Octomaps, and that it is more accurate to build ESDFs out of TSDFs than occupancy maps.
Robotics
1 code implementation • 17 Jul 2017 • Jemin Hwangbo, Inkyu Sa, Roland Siegwart, Marco Hutter
In this paper, we present a method to control a quadrotor with a neural network trained using reinforcement learning techniques.
Robotics
1 code implementation • 11 Sep 2017 • Inkyu Sa, Zetao Chen, Marija Popovic, Raghav Khanna, Frank Liebisch, Juan Nieto, Roland Siegwart
In this paper, we present an approach for dense semantic weed classification with multispectral images collected by a micro aerial vehicle (MAV).
no code implementations • 16 Sep 2017 • Fabian Blöchliger, Marius Fehr, Marcin Dymczyk, Thomas Schneider, Roland Siegwart
Then, we create a set of convex free-space clusters, which are the vertices of the topological map.
Robotics
no code implementations • 25 Sep 2017 • Mark Pfeiffer, Giuseppe Paolo, Hannes Sommer, Juan Nieto, Roland Siegwart, Cesar Cadena
This paper reports on a data-driven, interaction-aware motion prediction approach for pedestrians in environments cluttered with static obstacles.
Robotics
no code implementations • 28 Sep 2017 • Abel Gawel, Carlo Del Don, Roland Siegwart, Juan Nieto, Cesar Cadena
Our findings show that X-View is able to globally localize aerial-to-ground, and ground-to-ground robot data of drastically different view-points.
2 code implementations • 2 Oct 2017 • Helen Oleynikova, Zachary Taylor, Roland Siegwart, Juan Nieto
We perform extensive simulations to show that this system performs better than the standard approach of using an optimistic global planner, and also outperforms doing a single exploration step when the local planner is stuck.
Robotics
1 code implementation • 19 Oct 2017 • Alexander Millane, Zachary Taylor, Helen Oleynikova, Juan Nieto, Roland Siegwart, César Cadena
Central to our approach is the representation of the environment as a collection of overlapping TSDF subvolumes.
Robotics
no code implementations • 28 Nov 2017 • Philipp Oettershagen, Julian Förster, Lukas Wirth, Jacques Ambühl, Roland Siegwart
While this makes them promising candidates for large-scale aerial inspection missions, their structural fragility necessitates that adverse weather is avoided using appropriate path planning methods.
1 code implementation • 28 Nov 2017 • Thomas Schneider, Marcin Dymczyk, Marius Fehr, Kevin Egger, Simon Lynen, Igor Gilitschenski, Roland Siegwart
On the other hand, maplab provides the research community with a collection of multisession mapping tools that include map merging, visual-inertial batch optimization, and loop closure.
Robotics
no code implementations • 10 Dec 2017 • Philipp Oettershagen, Florian Achermann, Benjamin Müller, Daniel Schneider, Roland Siegwart
Overall, our initial research demonstrates the feasibility of 3D wind field prediction from a UAV and the advantages of wind-aware planning.
no code implementations • 19 Dec 2017 • Timo Hinzmann, Tim Taubner, Roland Siegwart
This paper proposes a computationally efficient method to estimate the time-varying relative pose between two visual-inertial sensor rigs mounted on the flexible wings of a fixed-wing unmanned aerial vehicle (UAV).
no code implementations • 11 Mar 2018 • Marius Huber, Timo Hinzmann, Roland Siegwart, Larry H. Matthies
In this work, we propose that the range error is cubic in range for stereo systems with integrated illuminators.
2 code implementations • 12 Mar 2018 • Helen Oleynikova, Zachary Taylor, Roland Siegwart, Juan Nieto
Micro-Aerial Vehicles (MAVs) have the advantage of moving freely in 3D space.
Robotics
1 code implementation • 13 Mar 2018 • Michel Breyer, Fadri Furrer, Tonci Novkovic, Roland Siegwart, Juan Nieto
We learn closed-loop policies mapping depth camera inputs to motion commands and compare different approaches to keep the problem tractable, including reward shaping, curriculum learning and using a policy pre-trained on a task with a reduced action set to warm-start the full problem.
Robotics
1 code implementation • 25 Apr 2018 • Renaud Dubé, Andrei Cramariuc, Daniel Dugas, Juan Nieto, Roland Siegwart, Cesar Cadena
While current methods extract descriptors for the single task of localization, SegMap leverages a data-driven descriptor in order to extract meaningful features that can also be used for reconstructing a dense 3D map of the environment and for extracting semantic information.
1 code implementation • 28 May 2018 • Marcin Dymczyk, Marius Fehr, Thomas Schneider, Roland Siegwart
This paper discusses a large-scale and long-term mapping and localization scenario using the maplab open-source framework.
no code implementations • 9 Jul 2018 • Guoxiang Zhou, Berta Bescos, Marcin Dymczyk, Mark Pfeiffer, José Neira, Roland Siegwart
Yet, in highly dynamic environments, like crowded city streets, problems arise as major parts of the image can be covered by dynamic objects.
no code implementations • 12 Jul 2018 • Marcin Dymczyk, Igor Gilitschenski, Juan Nieto, Simon Lynen, Bernhard Zeisl, Roland Siegwart
We propose LandmarkBoost - an approach that, in contrast to the conventional 2D-3D matching methods, casts the search problem as a landmark classification task.
1 code implementation • 30 Jul 2018 • Hermann Blum, Abel Gawel, Roland Siegwart, Cesar Cadena
Sensor fusion is a fundamental process in robotic systems as it extends the perceptual range and increases robustness in real-world operations.
1 code implementation • 4 Sep 2018 • Paul-Edouard Sarlin, Frédéric Debraine, Marcin Dymczyk, Roland Siegwart, Cesar Cadena
Many robotics applications require precise pose estimates despite operating in large and changing environments.
1 code implementation • 8 Sep 2018 • Marija Popovic, Teresa Vidal-Calleja, Gregory Hitz, Jen Jen Chung, Inkyu Sa, Roland Siegwart, Juan Nieto
Unmanned Aerial Vehicles (UAVs) represent a new frontier in a wide range of monitoring and research applications.
Robotics
1 code implementation • 20 Sep 2018 • Berta Bescos, José Neira, Roland Siegwart, Cesar Cadena
In this paper we present an end-to-end deep learning framework to turn images that show dynamic content, such as vehicles or pedestrians, into realistic static frames.
no code implementations • 26 Sep 2018 • Nikhil Bharadwaj Gosala, Andreas Bühler, Manish Prajapat, Claas Ehmke, Mehak Gupta, Ramya Sivanesan, Abel Gawel, Mark Pfeiffer, Mathias Bürki, Inkyu Sa, Renaud Dubé, Roland Siegwart
In autonomous racing, vehicles operate close to the limits of handling and a sensor failure can have critical consequences.
1 code implementation • 30 Sep 2018 • Ciro Potena, Raghav Khanna, Juan Nieto, Roland Siegwart, Daniele Nardi, Alberto Pretto
The combination of aerial survey capabilities of Unmanned Aerial Vehicles with targeted intervention abilities of agricultural Unmanned Ground Vehicles can significantly improve the effectiveness of robotic systems applied to precision agriculture.
3 code implementations • CVPR 2019 • Paul-Edouard Sarlin, Cesar Cadena, Roland Siegwart, Marcin Dymczyk
In this paper we propose HF-Net, a hierarchical localization approach based on a monolithic CNN that simultaneously predicts local features and global descriptors for accurate 6-DoF localization.
Ranked #3 on Visual Place Recognition on Berlin Kudamm
no code implementations • 12 Feb 2019 • Mathias Bürki, Lukas Schaupp, Marcin Dymczyk, Renaud Dubé, Cesar Cadena, Roland Siegwart, Juan Nieto
Changes in appearance is one of the main sources of failure in visual localization systems in outdoor environments.
Robotics
2 code implementations • IEEE ROBOTICS AND AUTOMATION LETTERS 2019 • Margarita Grinvald, Fadri Furrer, Tonci Novkovic, Jen Jen Chung, Cesar Cadena, Roland Siegwart, Juan Nieto
To autonomously navigate and plan interactions in real-world environments, robots require the ability to robustly perceive and map complex, unstructured surrounding scenes.
Robotics
no code implementations • 19 Mar 2019 • Lukas Schaupp, Mathias Bürki, Renaud Dubé, Roland Siegwart, Cesar Cadena
We introduce a novel method for oriented place recognition with 3D LiDAR scans.
Robotics
1 code implementation • 5 Apr 2019 • Hermann Blum, Paul-Edouard Sarlin, Juan Nieto, Roland Siegwart, Cesar Cadena
Deep learning has enabled impressive progress in the accuracy of semantic segmentation.
Ranked #13 on Anomaly Detection on Fishyscapes L&F (using extra training data)
4 code implementations • 13 May 2019 • Juraj Kabzan, Miguel de la Iglesia Valls, Victor Reijgwart, Hubertus Franciscus Cornelis Hendrikx, Claas Ehmke, Manish Prajapat, Andreas Bühler, Nikhil Gosala, Mehak Gupta, Ramya Sivanesan, Ankit Dhall, Eugenio Chisari, Napat Karnchanachari, Sonja Brits, Manuel Dangel, Inkyu Sa, Renaud Dubé, Abel Gawel, Mark Pfeiffer, Alexander Liniger, John Lygeros, Roland Siegwart
This paper presents the algorithms and system architecture of an autonomous racecar.
Robotics
1 code implementation • 23 Jun 2019 • Luigi Freda, Mario Gianni, Fiora Pirri, Abel Gawel, Renaud Dube, Roland Siegwart, Cesar Cadena
This target selection relies on a shared idleness representation and a coordination mechanism preventing topological conflicts.
no code implementations • 30 Jun 2019 • Simon Lynen, Bernhard Zeisl, Dror Aiger, Michael Bosse, Joel Hesch, Marc Pollefeys, Roland Siegwart, Torsten Sattler
Our approach spans from offline model building to real-time client-side pose fusion.
1 code implementation • 22 Jul 2019 • Rik Bähnemann, Nicholas Lawrance, Jen Jen Chung, Michael Pantic, Roland Siegwart, Juan Nieto
In this paper, we present a path planner for low-altitude terrain coverage in known environments with unmanned rotary-wing micro aerial vehicles (MAVs).
Robotics
no code implementations • 1 Aug 2019 • Nicolas Marchal, Charlotte Moraldo, Roland Siegwart, Hermann Blum, Cesar Cadena, Abel Gawel
Foreground objects are therefore detected as areas in an image for which the descriptors are unlikely given the background distribution.
no code implementations • 18 Aug 2019 • Danylo Malyuta, Christian Brommer, Daniel Hentzen, Thomas Stastny, Roland Siegwart, Roland Brockers
Vision-based precision landing is enabled by estimating the landing pad's pose using a bundle of AprilTag fiducials configured for detection from a wide range of altitudes.
no code implementations • 2 Sep 2019 • David Haldimann, Hermann Blum, Roland Siegwart, Cesar Cadena
There has been a remarkable progress in the accuracy of semantic segmentation due to the capabilities of deep learning.
2 code implementations • 20 Sep 2019 • Lukas Schmid, Michael Pantic, Raghav Khanna, Lionel Ott, Roland Siegwart, Juan Nieto
However, they are prone to local minima, resulting in sub-optimal trajectories, and sometimes do not reach global coverage.
2 code implementations • 27 Sep 2019 • Renaud Dubé, Andrei Cramariuc, Daniel Dugas, Hannes Sommer, Marcin Dymczyk, Juan Nieto, Roland Siegwart, Cesar Cadena
We therefore present SegMap: a map representation solution for localization and mapping based on the extraction of segments in 3D point clouds.
no code implementations • 4 Dec 2019 • Abel Gawel, Hermann Blum, Johannes Pankert, Koen Krämer, Luca Bartolomei, Selen Ercan, Farbod Farshidian, Margarita Chli, Fabio Gramazio, Roland Siegwart, Marco Hutter, Timothy Sandy
We present a fully-integrated sensing and control system which enables mobile manipulator robots to execute building tasks with millimeter-scale accuracy on building construction sites.
2 code implementations • 5 Dec 2019 • Florian Tschopp, Michael Riner, Marius Fehr, Lukas Bernreiter, Fadri Furrer, Tonci Novkovic, Andreas Pfrunder, Cesar Cadena, Roland Siegwart, Juan Nieto
Robust and accurate pose estimation is crucial for many applications in mobile robotics.
Robotics
1 code implementation • 29 Jan 2020 • Yu Liu, Jie Li, Xia Yuan, Chunxia Zhao, Roland Siegwart, Ian Reid, Cesar Cadena
We propose PALNet, a novel hybrid network for SSC based on single depth.
1 code implementation • 25 Feb 2020 • Julien Kindle, Fadri Furrer, Tonci Novkovic, Jen Jen Chung, Roland Siegwart, Juan Nieto
Mobile manipulation is usually achieved by sequentially executing base and manipulator movements.
no code implementations • 15 Mar 2020 • Sirish Srinivasan, Inkyu Sa, Alex Zyner, Victor Reijgwart, Miguel I. Valls, Roland Siegwart
Velocity estimation plays a central role in driverless vehicles, but standard and affordable methods struggle to cope with extreme scenarios like aggressive maneuvers due to the presence of high sideslip.
no code implementations • 20 Mar 2020 • Karen Bodie, Maximilian Brunner, Michael Pantic, Stefan Walser, Patrick Pfändler, Ueli Angst, Roland Siegwart, Juan Nieto
A fully actuated and omnidirectional tilt-rotor aerial system is used to show capabilities of the control and planning methods.
Robotics
no code implementations • 2 Apr 2020 • Kenneth Blomqvist, Michel Breyer, Andrei Cramariuc, Julian Förster, Margarita Grinvald, Florian Tschopp, Jen Jen Chung, Lionel Ott, Juan Nieto, Roland Siegwart
With humankind facing new and increasingly large-scale challenges in the medical and domestic spheres, automation of the service sector carries a tremendous potential for improved efficiency, quality, and safety of operations.
1 code implementation • 24 May 2020 • Andrei Cramariuc, Aleksandar Petrov, Rohit Suri, Mayank Mittal, Roland Siegwart, Cesar Cadena
Self-diagnosis and self-repair are some of the key challenges in deploying robotic platforms for long-term real-world applications.
no code implementations • 10 Aug 2020 • Timo Hinzmann, Tobias Stegemann, Cesar Cadena, Roland Siegwart
In this paper, we present our deep learning-based human detection system that uses optical (RGB) and long-wave infrared (LWIR) cameras to detect, track, localize, and re-identify humans from UAVs flying at high altitude.
no code implementations • 11 Aug 2020 • Timo Hinzmann, Roland Siegwart
This paper presents a framework for the localization of Unmanned Aerial Vehicles (UAVs) in unstructured environments with the help of deep learning.
no code implementations • 13 Aug 2020 • Cedric Le Gentil, Florian Tschopp, Ignacio Alzugaray, Teresa Vidal-Calleja, Roland Siegwart, Juan Nieto
The method's front-end extracts event clusters that belong to line segments in the environment whereas the back-end estimates the system's trajectory alongside the lines' 3D position by minimizing point-to-line distances between individual events and the lines' projection in the image space.
Robotics
no code implementations • NeurIPS Workshop TDA_and_Beyond 2020 • Simon Till Schönenberger, Anastasiia Varava, Vladislav Polianskii, Jen Jen Chung, Danica Kragic, Roland Siegwart
We present a Witness Autoencoder (W-AE) – an autoencoder that captures geodesic distances of the data in the latent space.
no code implementations • 19 Oct 2020 • Alexander Millane, Helen Oleynikova, Christian Lanegger, Jeff Delmerico, Juan Nieto, Roland Siegwart, Marc Pollefeys, Cesar Cadena
Localization of a robotic system within a previously mapped environment is important for reducing estimation drift and for reusing previously built maps.
Robotics
1 code implementation • 21 Oct 2020 • Felix Taubner, Florian Tschopp, Tonci Novkovic, Roland Siegwart, Fadri Furrer
In this paper, we introduce a novel learning-based approach to place recognition, using RGB-D cameras and line clusters as visual and geometric features.
no code implementations • 3 Nov 2020 • Julia Nitsch, Masha Itkina, Ransalu Senanayake, Juan Nieto, Max Schmidt, Roland Siegwart, Mykel J. Kochenderfer, Cesar Cadena
A mechanism to detect OOD samples is important for safety-critical applications, such as automotive perception, to trigger a safe fallback mode.
1 code implementation • 4 Nov 2020 • Le Chen, Yunke Ao, Florian Tschopp, Andrei Cramariuc, Michel Breyer, Jen Jen Chung, Roland Siegwart, Cesar Cadena
Visual-inertial systems rely on precise calibrations of both camera intrinsics and inter-sensor extrinsics, which typically require manually performing complex motions in front of a calibration target.
1 code implementation • 2020 Conference on Robot Learning 2020 • Florian Achermann, Andrey Kolobov, Debadeepta Dey, Timo Hinzmann, Jen Jen Chung, Roland Siegwart, Nicholas Lawrance
This model is then deployed for fast and accurate online interest point detection.
1 code implementation • 26 Nov 2020 • Mattia Segu, Margarita Grinvald, Roland Siegwart, Federico Tombari
Transferring the style from one image onto another is a popular and widely studied task in computer vision.
no code implementations • 5 Dec 2020 • Janis Postels, Hermann Blum, Yannick Strümpler, Cesar Cadena, Roland Siegwart, Luc van Gool, Federico Tombari
We find that this leads to improved OOD detection of epistemic uncertainty at the cost of ambiguous calibration close to the data distribution.
1 code implementation • 8 Dec 2020 • Daniel Dugas, Juan Nieto, Roland Siegwart, Jen Jen Chung
In this work, we design ways in which unsupervised learning can be used to assist reinforcement learning for robot navigation.
1 code implementation • 4 Jan 2021 • Michel Breyer, Jen Jen Chung, Lionel Ott, Roland Siegwart, Juan Nieto
General robot grasping in clutter requires the ability to synthesize grasps that work for previously unseen objects and that are also robust to physical interactions, such as collisions with other objects in the scene.
Robotics
no code implementations • 20 Jan 2021 • Weixuan Zhang, Lionel Ott, Marco Tognon, Roland Siegwart, Juan Nieto
However, the efficient and effective data collection for such a data-driven system on real robots is still an open challenge.
Robotics Systems and Control Systems and Control
no code implementations • 8 Feb 2021 • Joël Bachmann, Kenneth Blomqvist, Julian Förster, Roland Siegwart
This, despite the fact that the physical 3D spaces have a similar semantic structure to bodies of text: words are surrounded by words that are semantically related, just like objects are surrounded by other objects that are similar in concept and usage.
1 code implementation • 16 Feb 2021 • Florian Tschopp, Cornelius von Einem, Andrei Cramariuc, David Hug, Andrew William Palmer, Roland Siegwart, Margarita Chli, Juan Nieto
As a basis for a localization system we propose a complete on-board mapping pipeline able to map robust meaningful landmarks, such as poles from power lines, in the vicinity of the vehicle.
1 code implementation • CVPR 2021 • Giancarlo Di Biase, Hermann Blum, Roland Siegwart, Cesar Cadena
The inability of state-of-the-art semantic segmentation methods to detect anomaly instances hinders them from being deployed in safety-critical and complex applications, such as autonomous driving.
Ranked #3 on Anomaly Detection on Lost and Found (using extra training data)
1 code implementation • 25 Mar 2021 • Dominic Streiff, Lukas Bernreiter, Florian Tschopp, Marius Fehr, Roland Siegwart
Furthermore, 3D feature-based registration methods have never quite reached the robustness of 2D methods in visual SLAM.
no code implementations • 30 Mar 2021 • Timo Hinzmann, Roland Siegwart
This paper introduces SD-6DoF-ICLK, a learning-based Inverse Compositional Lucas-Kanade (ICLK) pipeline that uses sparse depth information to optimize the relative pose that best aligns two images on SE(3).
2 code implementations • 30 Apr 2021 • Robin Chan, Krzysztof Lis, Svenja Uhlemeyer, Hermann Blum, Sina Honari, Roland Siegwart, Pascal Fua, Mathieu Salzmann, Matthias Rottmann
State-of-the-art semantic or instance segmentation deep neural networks (DNNs) are usually trained on a closed set of semantic classes.
1 code implementation • 16 May 2021 • Margarita Grinvald, Federico Tombari, Roland Siegwart, Juan Nieto
The ability to simultaneously track and reconstruct multiple objects moving in the scene is of the utmost importance for robotic tasks such as autonomous navigation and interaction.
no code implementations • 4 Sep 2021 • Gianmario Fumagalli, Yannick Huber, Marcin Dymczyk, Roland Siegwart, Renaud Dubé
Camera anomalies like rain or dust can severelydegrade image quality and its related tasks, such as localizationand segmentation.
no code implementations • 20 Sep 2021 • Florian Tschopp, Juan Nieto, Roland Siegwart, Cesar Cadena
Introducing semantically meaningful objects to visual Simultaneous Localization And Mapping (SLAM) has the potential to improve both the accuracy and reliability of pose estimates, especially in challenging scenarios with significant view-point and appearance changes.
1 code implementation • 30 Sep 2021 • Yunke Ao, Le Chen, Florian Tschopp, Michel Breyer, Andrei Cramariuc, Roland Siegwart
Our approach models the calibration process compactly using model-free deep reinforcement learning to derive a policy that guides the motions of a robotic arm holding the sensor to efficiently collect measurements that can be used for both camera intrinsic calibration and camera-IMU extrinsic calibration.
no code implementations • 6 Oct 2021 • Boyang Sun, Jiaxu Xing, Hermann Blum, Roland Siegwart, Cesar Cadena
The proposed framework infers task failures by evaluating the individual prediction, across multiple visual perception tasks for different regions in an image.
1 code implementation • 18 Oct 2021 • Stefan Lionar, Lukas Schmid, Cesar Cadena, Roland Siegwart, Andrei Cramariuc
We present a novel 3D mapping method leveraging the recent progress in neural implicit representation for 3D reconstruction.
1 code implementation • 19 Jan 2022 • Kenneth Blomqvist, Jen Jen Chung, Lionel Ott, Roland Siegwart
In this work, we present a full object keypoint tracking toolkit, encompassing the entire process from data collection, labeling, model learning and evaluation.
no code implementations • 1 Mar 2022 • Chunwei Xing, Xinyu Sun, Andrei Cramariuc, Samuel Gull, Jen Jen Chung, Cesar Cadena, Roland Siegwart, Florian Tschopp
However, handcrafted topological descriptors are hard to tune and not robust to environmental noise, drastic perspective changes, object occlusion or misdetections.
1 code implementation • 23 Mar 2022 • Daniel Dugas, Olov Andersson, Roland Siegwart, Jen Jen Chung
In order to successfully solve the navigation task from only images, algorithms must be able to model the scene and its dynamics using only this channel of information.
no code implementations • 3 May 2022 • Michael Pantic, Cesar Cadena, Roland Siegwart, Lionel Ott
This work investigates the use of Neural implicit representations, specifically Neural Radiance Fields (NeRF), for geometrical queries and motion planning.
1 code implementation • 21 Jun 2022 • Hermann Blum, Marcus G. Müller, Abel Gawel, Roland Siegwart, Cesar Cadena
In order to operate in human environments, a robot's semantic perception has to overcome open-world challenges such as novel objects and domain gaps.
no code implementations • 28 Jun 2022 • Weixuan Zhang, Lionel Ott, Marco Tognon, Roland Siegwart
The recent development of novel aerial vehicles capable of physically interacting with the environment leads to new applications such as contact-based inspection.
1 code implementation • 17 Aug 2022 • Lukas Schmid, Mansoor Nasir Cheema, Victor Reijgwart, Roland Siegwart, Federico Tombari, Cesar Cadena
We further present an informative path planning method, leveraging the capabilities of our mapping approach and a novel scene-completion-aware information gain.
1 code implementation • 16 Sep 2022 • Samuel Looper, Javier Rodriguez-Puigvert, Roland Siegwart, Cesar Cadena, Lukas Schmid
In this work, we formalize the task of semantic scene variability estimation and identify three main varieties of semantic scene change: changes in the position of an object, its semantic state, or the composition of a scene as a whole.
no code implementations • 26 Sep 2022 • Kenneth Blomqvist, Lionel Ott, Jen Jen Chung, Roland Siegwart
Methods have recently been proposed that densely segment 3D volumes into classes using only color images and expert supervision in the form of sparse semantically annotated pixels.
1 code implementation • CVPR 2023 • Zhizheng Liu, Francesco Milano, Jonas Frey, Roland Siegwart, Hermann Blum, Cesar Cadena
Due to the mismatch between training and deployment data, adapting the model on the new scenes is often crucial to obtain good performance.
2 code implementations • 20 Mar 2023 • Kenneth Blomqvist, Francesco Milano, Jen Jen Chung, Lionel Ott, Roland Siegwart
In this work, we present a zero-shot volumetric open-vocabulary semantic scene segmentation method.
no code implementations • 28 Jul 2023 • Matthias Brucker, Andrei Cramariuc, Cornelius von Einem, Roland Siegwart, Cesar Cadena
We evaluate our method on a custom dataset featuring railway images with artificially augmented obstacles.
2 code implementations • 11 Sep 2023 • Haoran Chen, Kenneth Blomqvist, Francesco Milano, Roland Siegwart
In this paper, we propose to the best of our knowledge the first algorithm for open-vocabulary panoptic segmentation in 3D scenes.
no code implementations • 5 Nov 2023 • Nicolas Gorlo, Kenneth Blomqvist, Francesco Milano, Roland Siegwart
To build spatial AI systems that can quickly be taught about new objects, we need to effectively solve the problem of single-shot object detection, instance segmentation and re-identification.
1 code implementation • 11 Dec 2023 • Bin Yang, Patrick Pfreundschuh, Roland Siegwart, Marco Hutter, Peyman Moghadam, Vaishakh Patil
In this paper, we propose TULIP, a new method to reconstruct high-resolution LiDAR point clouds from low-resolution LiDAR input.
no code implementations • 18 Jan 2024 • Florian Achermann, Thomas Stastny, Bogdan Danciu, Andrey Kolobov, Jen Jen Chung, Roland Siegwart, Nicholas Lawrance
Real-time high-resolution wind predictions are beneficial for various applications including safe manned and unmanned aviation.
no code implementations • 14 Mar 2024 • Nicolaj Schmid, Cornelius von Einem, Cesar Cadena, Roland Siegwart, Lorenz Hruby, Florian Tschopp
Building upon Instant Neural Graphics Primitives with a Multiresolution Hash Encoding (Instant-NGP), VIRUS-NeRF incorporates depth measurements from ultrasonic and infrared sensors and utilizes them to update the occupancy grid used for ray marching.
no code implementations • 21 Mar 2024 • Francesco Di Felice, Alberto Remus, Stefano Gasperini, Benjamin Busam, Lionel Ott, Federico Tombari, Roland Siegwart, Carlo Alberto Avizzano
Estimating the pose of objects through vision is essential to make robotic platforms interact with the environment.
1 code implementation • 12 Apr 2024 • Maurits Reitsma, Julian Keller, Kenneth Blomqvist, Roland Siegwart
We propose an interpretable framework for reading analog gauges that is deployable on real world robotic systems.